| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
This instructions provides a solution for installing HP LaserJet P1102 drivers on macOS Sequoia (15.0+). The official HP drivers don't install on the latest macOS versions, but with a simple modification to bypass the operating system version check, you can get your printer working again.
mirror of The Ultimate Beginner's Guide to GPU Passthrough (Proxmox, Windows 10) by /u/cjalas
>Welcome all, to the first installment of my Idiot Friendly tutorial series! I'll be guiding you through the process of configuring GPU Passthrough for your Proxmox Virtual Machine Guests. This guide is aimed at beginners to virtualization, particularly for Proxmox users. It is intended as an overall guide for passing through a GPU (or multiple GPUs) to your Virtual Machine(s). It is not intended as an all-exhaustive how-to guide; however, I will do my best to provide you with all the necessary resources and sources for the passthrough process, from start to finish. If something doesn't work properly, please check /r/Proxmox, /r/Homelab, /r/VFIO, or
| $ vagrant up ; vagrant ssh | |
| Bringing machine 'default' up with 'virtualbox' provider... | |
| ==> default: Importing base box 'ubuntu/trusty64'... | |
| ==> default: Matching MAC address for NAT networking... | |
| ==> default: Checking if box 'ubuntu/trusty64' is up to date... | |
| ==> default: Setting the name of the VM: trusty64_default_1456413993437_55351 | |
| ==> default: Clearing any previously set forwarded ports... | |
| ==> default: Clearing any previously set network interfaces... | |
| ==> default: Preparing network interfaces based on configuration... | |
| default: Adapter 1: nat |
Modern versions of Windows support GPU paravirtualization in Hyper-V with normal consumer graphics cards. This is used e.g. for graphics acceleration in Windows Sandbox, as well as WSLg. In some cases, it may be useful to create a normal VM with GPU acceleration using this feature, but this is not officially supported. People already figured out how to do it with Windows guests though, so why not do the same with Linux? It should be easy given that WSLg is open source and reasonably well documented, right?
Well... not quite. I managed to get it to run... but not well.
Workplaces may enforce TOTP 2FA to be enabled Office 365 accounts, which require the Microsoft Authenticator app to be installed.
Regular TOTP applications (such as Aegis, Authy, or LastPass) cannot be used as Microsoft uses a proprietary scheme called phonefactor. Furthermore, the application requires Google Services Framework (GSF) to be installed (likely to provide device notifications), and will refuse to work when it is not present on the device.
Forunately, after the registration is complete, the underlying mechanism the app uses to generate TOTP codes is regular otpauth, and its secrets can be exported with a little bit of effort.
| using System; | |
| using System.Collections.Generic; | |
| using UnityEngine; | |
| [CreateAssetMenu(fileName = "AbilityData", menuName = "ScriptableObjects/AbilityData")] | |
| class AbilityData : ScriptableObject { | |
| public string label; | |
| public AnimationClip animationClip; | |
| [Range(0.1f, 4f)] public float castTime = 2f; |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
